Facial expression recognition using depth map estimation of light field camera

Tak-Wai SHEN, Hong FU, Junkai CHEN, W.K. Yu, C.Y. LAU, W.L. LO, Zheru CHI

Research output: Chapter in Book/Report/Conference proceedingChapters

4 Citations (Scopus)

Abstract

Facial expressions recognition has gained a growing attention from industry and also academics, because it could be widely used in many field such as Human Computer Interface (HCI) and medical assessment. In this paper, we evaluate the strength of the Light Field Camera for facial expression recognition. The light filed camera can capture the directions of the incoming light rays which is not possible with a conventional 2D camera. In addition, the light filed camera could estimates depth maps which provide further information to handle the facial expression recognition problem. Firstly, a new facial expression dataset is collected by the light field camera. The depth map is estimated and applied on Histogram Oriented Gradient (HOG) to encode these facial components as features. Then, a linear SVM is trained to perform the facial expression classification. Performance of the proposed approach is evaluated using the new dataset with estimated depth map. Experimental results show that significant improvements on accuracy are achieved as compared to the traditional approach. Copyright © 2016 IEEE.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC 2016)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages288-291
ISBN (Electronic)9781509027088
ISBN (Print)9781509027095
DOIs
Publication statusPublished - 2016

Citation

Shen, T.-W., Fu, H., Chen, J., Yu, W. K., Lau, C. Y., Lo, W. L., & Chi, Z. (2016). Facial expression recognition using depth map estimation of light field camera. In 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC 2016) (pp. 288-291). Piscataway, NJ: IEEE.

Keywords

  • Facial expression recognition
  • HOG features
  • Facial component detection
  • SVM
  • Light field camera

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